Improving observation-based modeling of other agents using tentative stereotyping and compactification through kd-tree structuring
Bibliographic record
Abstract
In this paper, we propose two improvements to modeling other agents based on Observed Situation-Action Pairs and the Nearest Neighbor Rule --reevaluative stereotyping with switching and compactification of observations through kd-tree structuring and the Pseudo-Approximate Nearest Neighbor search. On the one hand, tentative stereotype models allow for good predictions of a modeled agent's behavior even after few observations. Periodic reevaluations of the chosen stereotype and of the stereotyping process itself, in addition to the potential for switching between different stereotypes or to the observation based model aids in dealing with very similar but not identical stereotypes and agents that do not conform to any stereotype. On the other hand, reducing comparisons for the Nearest Neighbor Rule by observation compactification keeps the application of the model efficient even after many observations have been made. Our experiments show that tentative stereotyping significantly improves cases in which the original method performs badly and that reevaluations and switching fortify stereotyping against the potential risk of using an incorrect stereotype. For compactification, our experiments show that using the kd-tree for compactifying observations and the Pseudo-Approximate Nearest Neighbor search for retrieving a Nearest Neighbor improves modeling efficiency when observations are abundant, but is sometimes coupled with a loss of accuracy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".